<p>Monte Carlo simulations (MCS) are widely used to benchmark slope reliability; however, the process is computationally expensive, often requiring days or even weeks of analysis. This high cost arises primarily from two iterative processes: the identification of the critical failure surface and repeated simulations. The computational burden becomes even more significant when modeling spatial variability in soil properties using random fields. To address these challenges, this study introduces an efficient approach that reduces computation time while maintaining accuracy. The key innovation of this study is the classification of variables into static and dynamic categories to optimize computational efficiency. Static variables, such as slice dimensions and coordinates, are determined once during deterministic analyses and stored for reuse across all MCS samples. This eliminates the need for repeated recalculations of geometric parameters. Meanwhile, dynamic variables such as soil properties or surcharge loads, are specifically assigned to each sample, reflecting variability in material properties. The method further enhances efficiency by pre-generating a set of potential failure surfaces and storing all essential data required for safety factor calculations. As a result, the computational effort is significantly reduced by minimizing redundant operations during simulations. Through three case studies, the proposed approach demonstrates its efficiency, achieving accurate results in just over a day—even for random field simulations&#xa0;in two directions. These practical computational times make it feasible to establish benchmark solutions for slope reliability, significantly improving upon traditional MCS methods that typically require weeks of analysis.</p>